In today’s rapidly changing and fiercely competitive business landscape, companies are constantly seeking ways to stay ahead of the curve and drive innovation. One of the key ingredients to building a truly innovative culture is diversity and inclusion. By embracing a wide range of perspectives, experiences, and backgrounds, organizations can unlock creativity, drive progress, and ultimately, achieve greater success in their endeavors.
Case Study 1: Google
A prime example of a company that has prioritized diversity and inclusion in fostering an innovative culture is Google. The tech giant has made significant strides in promoting diversity within its workforce, with initiatives such as unconscious bias training, diversity recruiting efforts, and employee resource groups for underrepresented groups. By actively seeking out diverse talent and creating a culture of inclusivity, Google has been able to tap into a wealth of diverse perspectives and ideas, leading to groundbreaking innovations such as Google Maps, Gmail, and other products that have revolutionized the tech industry.
Case Study 2: Airbnb
Another company that has demonstrated the power of diversity and inclusion in driving innovation is Airbnb. The hospitality platform has made diversity and inclusion a core part of its company culture, with initiatives such as unconscious bias training, diversity and inclusion workshops, and partnerships with organizations that support underrepresented communities. By actively promoting diversity in its workforce and fostering a culture of inclusion, Airbnb has been able to attract a diverse range of talent, leading to innovative ideas such as the Experiences feature, which allows users to book unique activities and experiences hosted by locals around the world.
In both of these case studies, we see the tangible benefits of diversity and inclusion in building an innovative culture. By bringing together individuals from a variety of backgrounds, experiences, and perspectives, companies are able to foster a culture of creativity, collaboration, and innovation. Diverse teams are more likely to challenge the status quo, think outside the box, and come up with innovative solutions to complex problems. Inclusive cultures also create a sense of belonging and psychological safety, encouraging employees to share their ideas, take risks, and push the boundaries of what is possible.
Conclusion
The importance of diversity and inclusion in building an innovative culture cannot be overstated. Companies that prioritize diversity and inclusion are not only able to attract top talent, but also drive creativity, foster collaboration, and ultimately, achieve greater success in their industries. By embracing diversity and creating a culture of inclusion, organizations can unlock the full potential of their employees, drive innovation, and stay ahead of the competition in today’s rapidly evolving business landscape.
SPECIAL BONUS: The very best change planners use a visual, collaborative approach to create their deliverables. A methodology and tools like those in Change Planning Toolkit™ can empower anyone to become great change planners themselves.
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Given that innovation is responsible for roughly 85% of economic growth, it’s without a doubt a pretty big deal for the success of both individual organizations, as well as for the society at large.
However, to achieve the level of impact that many are looking for from innovation, you can’t simply “create something new”, and then just hope the results will come. You will need to commit to systematically pursuing those results by scaling viable ideas into products or businesses that create value – at scale.
That is of course easier said than done. If you think it’s hard to come up with innovations, just try scaling one up. In this article, we’ll explore the topic in more detail and provide you with actionable tips on how to actually scale an innovation.
What does it mean to scale an innovation?
To explain what it means to scale an innovation, let’s first take a step back and look at the lifecycle of an innovation.
To begin, every innovation starts from a rough idea or concept. Often you may have a specific goal in mind, or a problem to be solved, but sometimes it can just be a cool idea that you think could really make an impact. From there, you first need to validate that the idea makes sense, and then build a product or a service that meets a real need in the market.
With these steps taken care of, the next part is to scale the innovation. At this point, we have all the pieces in place to create value, but we haven’t yet unlocked that value for the vast majority of the available market.
So, as you may see from the chart above, scaling is the part where most of the value creation and impact comes from. With that said, we can define scaling an innovation as the process of expanding the presence and the use of the innovation to be as widespread as possible to maximize that impact.
Scaling innovation is the process of expanding the presence and the use of the innovation to be as widespread as possible to maximize the impact the innovation can have.
While on paper that sounds straightforward enough, it’s extremely important to first clarify the vision of what successful scaling looks like for your innovation, and what metrics you will use to measure your success here. For some, it might just be revenue or profit, for others it could be the number of customers or users, the impact you’ve delivered, and so on.
Most of these metrics are of course related, but when you start with the end in mind and gradually work backwards from there, you are much more likely to succeed because everyone in the organization will know what it actually is that you’re aiming for.
With that goal in mind, you can start narrowing in on the methods required to get there, which is what we’ll be focusing on next.
Dimensions of scaling an innovation
Traditionally, scaling innovation is seen as a matter of advancing the adoption, or the diffusion, of innovation. This is best visualized with a chart depicting the adoption curve, which you’ll find below.
The idea is that to scale an innovation, you need to cross that chasm and go from a few early adopters to the mainstream market where the volumes are significantly higher.
While that is certainly true, we can dig a bit deeper to understand scaling in a more nuanced, and more practical, way.
In reality, there are three dimensions to scaling an innovation.
Let’s look at each of them a little closer.
Scaling Up
First, scaling up is about creating the preconditions for scaling effectively.
Before we start talking about scaling up, we’ll assume that the basic prerequisites for scaling are in place, namely that there’s a clear vision and a product-market fit for your innovation, and that the market potential is large enough for there to be something to scale to, even if the market isn’t there today.
Assuming those prerequisites are there, you need to ensure that:
you can produce enough of the innovation to scale
you can do that efficiently enough to be financially and operationally viable
For some products, such as software and other immaterial goods, that first part is pretty straightforward. For others, such as most complex manufactured goods, even the first one will be a real challenge.
Having said that, the second part of being efficient enough will prove to be a challenge for virtually every innovation. Even for a software product, acquiring, serving, and retaining customers profitably at scale is often more difficult than people realize. For other, fundamentally less scalable goods and services, this is often excruciating.
In addition to these two more practical aspects, there’s a third and more ambiguous component to scaling up, and that is the social and institutional adoption of the innovation.
How well you scale up affects how large of a scale you can ultimately reach.
For example, with an innovation as mundane as the modern umbrella, men who used it were initially ridiculed. So, before the umbrella could really take off as an innovation, societal norms needed to change. In other cases, there may be regulatory hurdles or other institutional considerations that might need to be addressed before an innovation can ultimately scale.
Regardless of the specifics, scaling up is necessary for every innovation that wants to reach significant scale.
However, what many people don’t pay enough attention to is that how well you scale up affects how large of a scale you can ultimately reach. If you can’t produce the goods at volume, and at low enough of a price while still being profitable at a unit economics level, there’s an obvious limit to your potential to scale.
Scaling Out
Scaling out is what most people think of when it comes to scaling an innovation. It’s the geographical or demographical expansion of the innovation to a larger audience.
In its simplest form, scaling out simply means getting a wider market share and audience for the innovation within an existing market. As we covered earlier, this typically means moving from those early adopter market segments towards the mainstream.
Scaling out is what most people think of when it comes to scaling innovation as it’s where you expand the innovation to a larger audience.
However, it doesn’t have to be limited to just that. Sometimes the same products or services can be sold and used in other geographical areas, or even in other industries or entirely different use cases, both of which unlock new markets and additional demand, and thus lead to a larger impact for the innovation. A well-known example of this is Tesla using their experience and innovations in electric car batteries to expand to stationary energy storage.
Regardless of which path you choose, often these efforts to scale out to new segments or industries do require additional work to adapt the innovation or its positioning to the differing characteristics of these new segments, markets, and audiences.
Scaling out to new market segments can increase complexity a lot, so be mindful of the operational implications of your strategic decisions here.
This naturally adds complexity, which makes the scaling up part we covered earlier more challenging. So, be mindful of how you scale out and what the operational implications of your strategic decisions here will be.
Scaling Deep
The third, and the least well-known method for scaling innovation is scaling deep. This essentially means that you unlock more impact for your innovation by expanding and maximizing the use of it, typically for the people who already have access to it.
This usually requires you to either change people’s behavior to increase usage, or alternatively come up with innovative means for improving the utilization rate by enabling more people to make use of the same assets. Scaling deep is partly a matter of culture and mindset, and partly a more practical matter of having the right components in place for enabling and encouraging active use of the innovation.
A classic, albeit somewhat controversial example of the first type would be social media algorithms. They are designed to provide users with engaging content to keep them entertained and thus stay in the service for longer, which leads to more revenue from the same number of users.
An example of the second type would be cloud computing. By adding network, virtualization, and software layers on top of the computing hardware, cloud providers can get more use out of the same hardware, which unlocks value for both the service provider and the customers.
This is how Amazon not just significantly reduced costs in one of their major cost centers, IT infrastructure, but actually turned that into Amazon Web Services (AWS), an additional growth business that now accounts for the majority of the profits for the entire organization.
Scaling deep is about unlocking more impact for your innovation by expanding and maximizing the use of it. This can help reduce the need to scale up or out, or alternatively maximize the impact from doing so.
Scaling Deep can reduce the need to scale up or out, or alternatively, maximize the impact from doing so. As such, it’s an excellent compliment for most innovations. However, it’s just that: a compliment. Your primary method of scaling should always be either to Scale Up or Scale Out depending on whether your bottleneck is more on the supply or demand side.
Even in the case of AWS, which has created entirely new vectors for scaling out and has dramatically subsidized their costs for scaling up, it obviously wouldn’t have been possible without Amazon already being at significant scale.
What’s the takeaway? These dimensions are distinct but very much intertwined.
If you can scale on all three of these dimensions in a coordinated way, you will not only be much more likely to achieve significant scale with your innovation in the first place, but also maximize the potential for scale and impact from those efforts. If you build momentum on one of the dimensions, some of that momentum will carry over to the other dimensions, which again helps you accelerate change going forward.
As such, pay attention to each of these dimensions and try to consider all of them in your plans to scale innovation. That doesn’t mean you should focus on all three from the get-go, on the contrary, but planning with the big picture in mind can allow you to make much more educated decisions.
Scaling innovation in practice
As we’ve established above, there unfortunately isn’t a one-size fits all solution to scaling innovation.
Achieving breakthrough success with an innovation, which is the goal of scaling innovation, always requires many related and adjacent (usually more incremental) innovations.
This is an extremely common pattern that you will see happening over and over again if you just start paying attention to it. Square co-founder Jim McKelvey has done a great job in describing that in more detail in his recent book called the Innovation Stack.
A well-known example is the lightbulb. Edison patented his famous design back in 1879, but most households didn’t yet have access to electricity, so it wasn’t something they could benefit from. It took countless other innovations and another 45 years before even half of US homes had one, even though the benefits were obvious.
In practice, scaling an innovation is simply an iterative and exploratory process where you focus on eliminating whatever bottleneck is preventing you from scaling, one by one. And, as we saw in the example of the lightbulb, sometimes these can be much bigger and more fundamental than you may think at first.
Often you can just copy solutions other people have already used for the same or a similar problem (which you should always go for if you can), but many times you will also need to innovate something completely new and occasionally even go beyond your core product.
With that said, there are some common patterns that can be helpful for structuring your thinking when faced with some of these bottlenecks. However, as each innovation is ultimately new, and thus unique, these won’t necessarily fit every case.
Having said that, we’ll share one framework for each dimension of scaling below. We’ve also created a toolkit that includes the frameworks as editable templates, along with some examples and other supporting material, which you can download here.
Demand side
For most organizations and innovations, the demand side is likely the source of most bottlenecks.
The way we see it, this is not just about drumming up interest and demand for your product, but also about making sure that it fits the needs and budgets of the buyers in your market. And of course, you need to make sure you’re in a market, or at least one that has the potential to become, large enough to accommodate your scaling efforts.
Unlike what people often think, product-market fit isn’t enough for a business to be scalable. You also need to have the right business and operating models, as well as use the right channels.
In other words, scaling out isn’t just about product-market fit, as people often mistakenly think. You also need to have the right business and operating models and use the right channels. Brian Balfour has written an excellent five-part series about this, which I highly recommend you read.
The basic idea is pretty simple: your business needs to align all of these aspects in a cohesive manner to be able to scale. If even one of them is wrong, growth will feel like, as Balfour puts it, “pushing a boulder uphill”. It will take way too much capital, effort, and time. However, get the four elements right together, and the growth will come naturally.
What’s important to understand here is that the model isn’t a static picture you just do once. If the market changes, or you run into challenges that force you to change one of these elements, you’ll need to review each element and make sure the big picture still works.
Supply side
For some products and businesses, especially those with physical products, the supply side often becomes a key consideration.
Here, the bottlenecks can be extremely varied, and dependences on external suppliers can lead to challenges that are hard to overcome.
In general, what top innovators do differently from the rest of the companies is that they almost always vertically integrate their value chain as they are working towards scaling up.
There are many benefits to this approach, such as reduced overhead, but the key differences are in increased quality, and most importantly, the company’s ability to control their own destiny and innovate more freely because they’re not being constrained by their supply chain.
Top innovators vertically integrate their value chain to address bottlenecks and turn cost centers into additional sources of growth and profit.
The classic example is Apple, and the way that they control both the hardware and software of their products. In recent years, they’ve been increasing that integration in both directions. They’re moving upstream to offer more services on top of their operating systems, as well as downstream by designing their own processors, which has provided them with a big performance advantage.
However, there are many others. Amazon, Microsoft, Tesla, Google, Netflix, Nvidia, and pretty much every innovative company is trying to do the same in the scope of their own business.
The basic idea is again simple: if a part of your supply chain becomes a major bottleneck, or is a major cost center, you should try to take control of those parts to address the bottlenecks and turn cost centers into additional sources of growth and profit, just like Amazon has done with AWS, but also warehousing and shipping.
That isn’t to say that vertical integration wouldn’t be challenging or have downsides. It certainly is and does. Because of these limitations, it’s generally advisable to only vertically integrate to the parts of your supply chain that either are a clear bottleneck or could become a key competitive advantage for you. However, top innovators often have little choice but to take these steps if they want to move fast enough and have enough control to be able to scale their innovation to its full potential.
Another key consideration on the supply side is simply the architecture of your products and services, and the process you have for delivering them. It’s obviously much easier to have a scalable architecture and automated processes for purely software or content focused businesses, but how you craft these does play a huge role for complex physical products too.
This is again a very extensive topic on its own, but the goal should be to try to make the manufacturing, delivery, and service of your products as seamless and scalable as possible. As with everything else we’ve discussed so far, this too is an iterative process.
However, to provide you with a slightly more practical framework to get started, here’s Elon Musk explaining how he’s learned to approach this topic after his early struggles of trying to do that with the extremely complex products at SpaceX and Tesla.
While Musk specifically talks about the process in the scope of engineering for scale, these same principles also apply to your organization and internal processes too.
And, as Musk explained in the video, it’s easy to get tempted by the promises of optimizing for efficiency and automation, but if you haven’t addressed the big picture first, these will often end up just being a big waste of time and money.
So, make sure to start by first eliminating those unnecessary requirements and parts or tasks, and try to simplify the design before you focus too much on optimizing for efficiency and automating.
Utilization
In addition to supply and demand, we still have the third dimension of utilization to cover. The idea with this “scaling deep” part is to find creative ways to make the most out of existing supply to either unlock new demand, maximize the utilization of those assets, or simply to increase your customer retention by finding ways to get more value for them from your products.
As you may have guessed by now, the specifics vary quite a lot on a case-by-case basis, but the flowchart below can hopefully serve as a starting point for your efforts in this area.
To summarize, there are three common paths you may take here.
The first is to find ways to increase the usage of assets that are only being used a fraction of the time through practices such as asset sharing and virtualization.
The second is to move from one-off purchases to a subscription to eliminate friction and increase the usage of the services.
The third is to find additional ways to expand the use of the product. This is usually done either by finding new value-adding uses for the same product, or simply by activating usage through means such as improved quality, usability, better communication etc.
However, sometimes it might even be necessary to work around tougher and more pervasive issues, such as regulatory considerations or even the changing of societal norms.
While increased utilization isn’t often that glamorous or exciting, it can really make a difference in making your business and operating models efficient enough to allow you to scale volume faster and more sustainably.
Conclusion
Scaling an innovation won’t be easy. It will always take years, and an endless amount of hard work with an extreme focus on solving each and every bottleneck standing in your way.
Hopefully you’ll find some of the frameworks and playbooks we’ve introduced in this article useful for shaping your thinking, and for building your organization and processes, but you’ll inevitably come across plenty of challenges where you’ll just need to figure out the solutions yourself. Still, if you want to truly succeed with innovation, that’s what you’re in for.
So, be prepared for those challenges, and be realistic with your expectations and timelines. For example, the “growth gap” can easily sneak up on your organization if top management has unrealistic expectations for the financial returns of innovation.
In general, large organizations have some disadvantages, but they also have huge advantages when it comes to scaling an innovation, so look for ways to leverage those advantages to your benefit.
And finally, make sure to surround yourself with top talent that’s prepared for the ride. Scaling innovation is teamwork, and it takes a special kind of a team to pull it off. You need people that are used to constant change, have a growth mindset, and the skills needed to solve whatever problems your domain may have.
As mentioned, scaling innovation is a journey that happens in small increments, and at times, it will feel frustrating. But if your team persists, keeps on learning and solving problems, you can eventually close in on whatever the full potential of your innovation is.
Image credits: Pexels, Viima
This article was originally published in Viima’s blog.
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How Collaborative Consumption Drives Sustainability
GUEST POST from Art Inteligencia
In recent years, the concept of the sharing economy has gained significant traction, with many individuals and businesses embracing the idea of collaborative consumption. This shift towards sharing resources, goods, and services is not only changing the way we consume, but also driving sustainability efforts across various industries. By redefining traditional notions of ownership and promoting a culture of sharing, collaborative consumption is proving to be a key driver in the fight against environmental degradation and resource depletion.
Case Study 1: Uber and Lyft
One of the most well-known examples of collaborative consumption is the rise of ride-sharing platforms such as Uber and Lyft. These services have revolutionized the way people commute in urban areas, providing a more efficient and cost-effective alternative to traditional taxi services. By connecting riders with drivers who are already heading in the same direction, ride-sharing platforms reduce the number of cars on the road, leading to decreased congestion and lower carbon emissions. In addition, the sharing of rides helps to optimize the use of existing resources, making transportation more sustainable in the long run.
Case Study 2: Airbnb
Another compelling case study of collaborative consumption driving sustainability is Airbnb, the popular accommodation-sharing platform. By enabling individuals to rent out their spare rooms or entire homes to travelers, Airbnb promotes the efficient use of existing housing stock and reduces the need for new hotel developments. This not only benefits hosts financially but also helps to alleviate the strain on local infrastructure and resources. Additionally, Airbnb encourages a more personal and authentic travel experience, fostering connections between hosts and guests and promoting cultural exchange.
Conclusion
Overall, the sharing economy presents a promising avenue for promoting sustainability and reducing the environmental impact of our consumption habits. By embracing the principles of collaborative consumption, individuals and businesses can contribute to a more sustainable future while also benefiting from increased efficiency and cost savings. As we navigate the challenges of climate change and resource scarcity, tapping into the sharing economy may just be the key to creating a more resilient and equitable society for generations to come.
Bottom line: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.
Image credit: Pixabay
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In the regulatory filing for Facebook’s 2012 IPO, Mark Zuckerberg included a letter outlining his management philosophy. Entitled, The Hacker Way, it encapsulated much of the zeitgeist. “We have a saying,” he wrote. “‘Move fast and break things.’ The idea is that if you never break anything, you’re probably not moving fast enough.”
At around the same time, Katalin Karikó was quietly plodding away in her lab at the University of Pennsylvania. She had been working on an idea since the early 1990s and it hadn’t amounted to much so far, but was finally beginning to attract some interest. The next year she would join a small startup named BioNTech to commercialize her work and would continue to chip at the problem.
Things would accelerate in early 2020, when Karikó’s mRNA technology was used to design a coronavirus vaccine in a matter of mere hours. Just as Daniel Kahneman explained that there are fast and slow modes of thinking, the same can be said about innovating. The truth is that moving slowly is often underrated and that moving fast can sometimes bog you down.
The Luxury Of Stability
Mark Zuckerberg had the luxury of being disruptive because he was working in a mature, stable environment. His “Hacker Way” letter showed a bias for action over deliberation in the form of “shipping code,” because he had little else to worry about. Facebook could be built fast, because it was built on top of technology that was slowly developed over decades.
The origins of modern computing are complex, with breakthroughs in multiple fields eventually converging into a single technology. Alan Turing and Claude Shannon provided much of the theoretical basis for digital computing in the 1930s and 40s. Yet the vacuum tube technology at the time only allowed for big, clunky machines that were very limited.
A hardware breakthrough came in 1948, when John Bardeen, William Shockley and Walter Brattain invented the transistor, followed by Jack Kilby and Robert Noyce’s development of the integrated circuit in the late 1960s. The first computers were connected to the Internet a decade later and, a generation after that, Tim Berners-Lee invented the World Wide Web.
All of this happened very slowly but, by the time Mark Zuckerberg became aware of it all, it was just part of the landscape. Much like older generations grew up with the Interstate Highway System and took for granted that they could ride freely on it, Millennial hackers grew up in a period of technological, not to mention political, stability.
The Dangers Of Disruption
Mark Zuckerberg founded Facebook with a bold idea. “We believe that a more open world is a better world because people with more information can make better decisions and have a greater impact,” he wrote. That vision was central to how he built the company and its products. He believed that enabling broader and more efficient communication would foster a deeper and more complete understanding.
Yet the world looks much different when your vantage point is a technology company in Menlo Park, California then it does from, say, a dacha outside Moscow. If you are an aging authoritarian who is somewhat frustrated by your place in the world rather than a young, hubristic entrepreneur, you may take a dimmer view on things.
For many, if not most, people on earth, the world is often a dark and dangerous place and the best defense is often to go on offense. From that vantage point, an open information system is less an opportunity to promote better understanding and more of a vulnerability you can leverage to exploit your enemy.
In fact, the House of Representatives Committee on Intelligence found that agents of the Russian government used the open nature of Facebook and other social media outlets to spread misinformation and sow discord. That’s the problem with moving fast and breaking things. If you’re not careful, you inevitably end up breaking something important.
This principle will become even more important in the years ahead as the potential for serious disruption increases markedly.
The Four Disruptive Shifts Of The Next Decade
While the era that shaped millennials like Mark Zuckerberg was mostly stable, the next decade is likely to be one of the most turbulent in history, with massive shifts in demography, resources, technology and migration. Each one of these has the potential to be destabilizing, the confluence of all four courts disaster and demands that we tread carefully.
Consider the demographic shift caused by the Millennials and Gen Z’ers coming of age. The last time we had a similar generational transition was with the Baby Boomers in the 1960s, which saw more than its share of social and political strife. The shift in values that will take place over the next ten years or so is likely to be similar in scale and scope.
Yet that’s just the start. We will also be shifting in resources from fossil fuels to renewables, in technology from bits to atoms and in migration globally from south to north and from rural to urban areas. The last time we had so many important structural changes going on at once it was the 1920s and that, as we should remember, did not turn out well.
It’s probably no accident that today, much like a century ago, we seem to yearn for “a return to normalcy.” The past two decades have been exhausting, with global terrorism, a massive financial meltdown and now a pandemic fraying our nerves and heightening our sense of vulnerability.
Still, I can’t help feeling that the lessons of the recent past can serve us well in creating a better future.
We Need To Rededicate Ourselves Tackling Grand Challenges
In Daniel Kahneman’s book, Thinking, Fast and Slow, he explained that we have two modes of thinking. The first is fast and intuitive. The second is slow and deliberative. His point wasn’t that one was better than the other, but that both have their purpose and we need to learn how to use both effectively. In many ways, the two go hand-in-hand.
One thing that is often overlooked is that to think fast effectively often takes years of preparation. Certain professions, such as surgeons and pilots, train for years to hone their instincts so that they will be able to react quickly and appropriately in an emergency. In many ways, you can’t think fast without first having thought slow.
Innovation is the same way. We were able to develop coronavirus vaccines in record time because of the years of slow, painstaking work by Katalin Karikó and others like her, much like how Mark Zuckerberg was able to “move fast and break things” because of the decades of breakthroughs it took to develop the technology that he “hacked.”
Today, as the digital era is ending, we need to rededicate ourselves to innovating slow. Just as our investment in things like the human genome project has returned hundreds of times what we put into it, our investment in the grand challenges of the future will enable countless new (hopefully more modest) Zuckerbergs to wax poetic about “hacker culture.”
Innovation is never a single event. It is a process of discovery, engineering and transformation and those things never happen in one place or at one time. That’s why we need to innovate fast and slow, build healthy collaborations and set our sights a bit higher.
In today’s highly competitive business landscape, customer experience has become a crucial differentiator for companies looking to stand out and attract and retain loyal customers. Design thinking, a user-centered approach to innovation, has emerged as a powerful tool for enhancing customer experience and building customer loyalty.
Design thinking is a human-centered methodology grounded in empathy and creativity. By putting the customer at the center of the design process, companies can gain a deeper understanding of their needs, preferences, and pain points, which in turn allows them to create products and services that truly meet their customers’ expectations.
Case Study 1: Apple
One company that has successfully leveraged design thinking to enhance customer experience is Apple. Apple’s commitment to design excellence and user-centric innovation has helped the company build a loyal customer base that is willing to pay a premium for its products. Apple’s focus on simplicity, intuitive design, and seamless integration across its product ecosystem has set it apart from competitors and made it a leader in customer experience.
Case Study 2: Airbnb
Another company that has embraced design thinking to drive customer loyalty is Airbnb. By taking a human-centered approach to service design, Airbnb has created a platform that not only meets customers’ needs for affordable and unique accommodation but also fosters a sense of community and connection among users. Airbnb’s focus on personalization, transparency, and trust has helped the company build a devoted customer base that returns to the platform again and again.
Conclusion
Design thinking can be a powerful tool for companies looking to enhance customer experience and build customer loyalty. By putting the customer at the center of the design process, companies can gain valuable insights into their needs and preferences, leading to the creation of products and services that truly resonate with customers. Companies like Apple and Airbnb have demonstrated the impact of design thinking on customer experience and loyalty, setting a powerful example for businesses looking to differentiate themselves in the market. As competition continues to intensify, companies that prioritize design thinking will be well-positioned to thrive in an increasingly customer-centric world.
SPECIAL BONUS: The very best change planners use a visual, collaborative approach to create their deliverables. A methodology and tools like those in Change Planning Toolkit™ can empower anyone to become great change planners themselves.
Image credit: Unsplash
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Intrapreneurs are employees trying to act like entrepreneurs, i.e. pursuing opportunity in their organizations with scarce resources with the goal of creating user defined value through the deployment of innovation. Many run into a brick wall.
Remember, it is easier to ask for forgiveness than for permission.
Do any job that needs to make your project work, regardless of your job description.
Come to work each day willing to be fired.
Recruit a strong team.
Ask for advice before resources.
Forget pride of authorship, spread credit wisely
When you bend the rules, keep the best interests of the company and its customers in mind.
Honor your sponsors
Underpromise and overdeliver
Be true to your goals, but realistic about ways to achieve them.
We’ve heard #1 a lot and it has become part of the lore of intrapreneurship and organizational behavior. But, is it really a good idea? It depends, and here are some reasons why:
Every organization, hospital and university has a culture of risk. Some cut you some slack. Some don’t.
It depends on the risk involved. Andrew Gove of Intel advised to ask for foregiveness, but don’t drill holes below the water line.
Sometimes, it ‘s better to keep what you are doing secret so as not to expose your idea too soon to the organizational immune system or people who are out to torpedo your success.
It takes a while to get your idea ready for prime time and validate assumptions. Better to fail early and off the radar than flop big.
Getting the resources you need will require imagination and political savvy. Sometimes that requires stealth and cunning.
Most organizations have archaic systems for prioritizing innovation or a new product portfolio. Asking for permission just puts you in dysfunctional queue.
Better to deliver your idea with as much value added as possible.
You are not the only one with the responsibility of moving your idea forward. Think about your team members and sponsors who have their necks out too.
One swallow does not a summer make. Even if you roll out a successful idea, people are going to want to know what you have done for them lately. Better to have a pipeline of products in development before launch. Platforms are more attractive than products.
Building sustainability takes time and is sometimes done better off the radar. Once you have a successful internal venture, people will come to you to take credit.
There are two kinds of innovators. Permission seekers start with the rules, create ecosystems that conform to them, create business models that are new or different and that foster innovation. Forgiveness seekers, do the same, but in reverse. They use technologies that have reached a coherence tipping point to create business models and ecosystems and then drive to change the rules to allow them to scale.
There is a lot to recommend stealth innovation. Beware of making too much noise and make it low impact at the beginning. Don’t use words, like “center”, “institute” or “innovation” that are likely to mobilize hostiles with competing interests. Practice digipreneur guerilla tactics. Watch out for snipers.
Arming yourself with anti-radar technology is usually a smart move. However, if you get shot down over enemy territory it might be hard to find you and you will be placing your search and rescue team members in jeopardy. Think twice before flying over hostile territory without a survival plan.
In today’s fast-paced work environment, employee engagement plays a crucial role in driving productivity and job satisfaction. With the rapid advancements in artificial intelligence (AI) technology, organizations have a unique opportunity to leverage AI tools to enhance employee engagement and create a more productive and fulfilling workplace.
Case Study 1: Chatbots as Virtual Mentors
One innovative way organizations are using AI to improve employee engagement is through the use of virtual chatbots as mentors. These chatbots are programmed to provide guidance, support, and feedback to employees in real time, helping them navigate challenges and develop their skills.
For example, a large tech company implemented a virtual mentor chatbot for its customer service team. The chatbot was programmed to provide on-the-job training, answer questions, and offer personalized feedback based on the employee’s performance. As a result, employees felt more supported and engaged in their roles, leading to an increase in productivity and job satisfaction.
Case Study 2: AI-Driven Performance Management
Another way AI is transforming employee engagement is through AI-driven performance management systems. These systems use algorithms and data analytics to provide real-time insights into employee performance, leading to more personalized feedback and development opportunities.
A leading financial services firm implemented an AI-driven performance management system that analyzed employee data, such as productivity metrics and feedback, to identify areas for improvement and growth. The system then provided targeted feedback and recommendations to help employees enhance their skills and performance.
As a result, employees felt more engaged and empowered to take ownership of their development, leading to higher levels of job satisfaction and productivity across the organization.
Conclusion
AI has the potential to revolutionize employee engagement by providing personalized support, feedback, and development opportunities. By leveraging AI tools like virtual mentors and performance management systems, organizations can create a more engaging and fulfilling workplace that drives productivity and job satisfaction. It is essential for organizations to embrace AI as a tool to enhance employee engagement and create a more productive and successful work environment.
Bottom line: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.
Image credit: Pixabay
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Using Game Elements to Boost Engagement and Creativity
GUEST POST from Chateau G Pato
In today’s fast-paced and competitive business environment, companies are constantly looking for innovative ways to engage and motivate their employees. One method that has gained popularity in recent years is gamification – the use of game elements and principles in non-game contexts to drive desired behaviors. By incorporating elements such as points, badges, leaderboards, and rewards into everyday tasks and processes, organizations can increase employee engagement, productivity, and creativity.
Case Study 1: Salesforce
One company that has successfully implemented gamification in the workplace is Salesforce. The global customer relationship management software company uses a gamified platform called “Trailhead” to train and motivate its employees. Trailhead allows employees to earn points, badges, and rewards for completing training modules and challenges, creating a sense of accomplishment and friendly competition among teams. As a result, employees are more invested in their learning and development, leading to increased productivity and retention.
Case Study 2: Microsoft
Another example of gamification in the workplace is Microsoft’s “The Ribbon Hero” game. Designed to help employees improve their skills in using Microsoft Office applications, the game challenges players to complete tasks and challenges within the programs, earning points and moving up levels as they progress. By making learning fun and interactive, Microsoft has seen a significant increase in employee engagement and proficiency with their software tools.
Conclusion
Incorporating gamification into the workplace can have numerous benefits for organizations, including increased employee engagement, motivation, and creativity. By tapping into employees’ natural desire for competition, recognition, and achievement, companies can create a more dynamic and fulfilling work environment. As technology continues to advance and the workforce becomes increasingly diverse and digital, gamification will play an essential role in driving innovation and success in the modern workplace.
SPECIAL BONUS: The very best change planners use a visual, collaborative approach to create their deliverables. A methodology and tools like those in Change Planning Toolkit™ can empower anyone to become great change planners themselves.
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In today’s technologically driven world, data plays a crucial role in almost every sector, and healthcare is no exception. With the rise of electronic health records and wearable devices, the healthcare industry has access to a vast amount of patient data. Big data analytics in healthcare is revolutionizing the way patient care is delivered by unlocking valuable insights that can lead to better outcomes.
One of the key areas where big data analytics is making a significant impact is in personalized medicine. By analyzing large datasets of patient information, healthcare providers can tailor treatment plans to individual patients based on their unique characteristics and medical history. This allows for more targeted and effective treatments, ultimately leading to better outcomes for patients.
Case Study 1: Mayo Clinic
A notable case study showcasing the benefits of big data analytics in personalized medicine is the work being done by the Mayo Clinic. By leveraging advanced analytics tools, the Mayo Clinic has been able to identify patterns in patient data to predict disease progression and customize treatment plans. This approach has resulted in improved patient outcomes and reduced healthcare costs, highlighting the potential of big data analytics to transform the healthcare landscape.
Another area where big data analytics is making a difference in healthcare is in population health management. By analyzing data from large groups of patients, healthcare providers can identify trends and patterns that can help improve overall health outcomes for entire communities. This proactive approach allows for early intervention and targeted interventions to prevent the onset of chronic diseases and improve population health.
Case Study 2: Pittsburgh Medical Center (UPMC)
An excellent example of the success of population health management through big data analytics is the partnership between the University of Pittsburgh Medical Center (UPMC) and IBM Watson Health. By combining UPMC’s wealth of patient data with IBM’s advanced analytics capabilities, the organizations have been able to develop predictive models that identify patients at risk for various health conditions and tailor interventions to prevent or manage these conditions effectively. This partnership has led to better health outcomes for patients and reduced healthcare costs, demonstrating the power of big data analytics in improving population health.
Conclusion
Big data analytics in healthcare is transforming the way patient care is delivered by unlocking valuable insights that lead to better outcomes. By leveraging advanced analytics tools, healthcare providers can personalize treatment plans, improve population health, and ultimately enhance the overall quality of care. The success stories of organizations like the Mayo Clinic and UPMC demonstrate the potential of big data analytics to revolutionize healthcare and improve patient outcomes. By embracing this technology and incorporating it into everyday practice, healthcare providers can truly unlock the full potential of big data analytics and provide better care for patients.
Bottom line: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.
Image credit: Pixabay
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Key considerations for planning a customer-centric digital transformation
GUEST POST from Chateau G Pato
In today’s rapidly evolving business landscape, digital transformation has become a critical imperative for organizations looking to stay competitive and relevant. However, many companies often overlook one crucial aspect of this process – understanding and meeting the needs and expectations of their customers.
A customer-centric approach to digital transformation is essential in ensuring that the changes implemented align with what customers want and need. By putting the customer at the center of the transformation journey, businesses can drive greater customer satisfaction, loyalty, and ultimately, business growth.
To effectively plan a customer-centric digital transformation, organizations must first gain a deep understanding of their customers’ needs, preferences, and pain points. This can be done through a variety of methods, such as customer surveys, focus groups, and data analytics. By collecting and analyzing this information, businesses can identify key trends and insights that can inform their digital transformation strategy.
Case study 1: Amazon
Amazon is a prime example of a company that has successfully implemented a customer-centric digital transformation. By leveraging data analytics and machine learning, Amazon is able to personalize the online shopping experience for each customer, recommend products based on their browsing history, and offer fast and convenient delivery options. This customer-centric approach has helped Amazon cement its position as the largest online retailer in the world, with a loyal customer base and strong brand reputation.
Case study 2: Starbucks
Starbucks is another company that has prioritized customer needs and expectations in its digital transformation efforts. By investing in its mobile app and loyalty program, Starbucks has made it easier for customers to order and pay for their favorite drinks, earn rewards, and receive personalized offers. This has not only improved the customer experience but also increased customer engagement and loyalty. As a result, Starbucks has seen significant growth in its digital sales and overall revenue.
Conclusion
Understanding and meeting customer needs and expectations are essential considerations for planning a successful customer-centric digital transformation. By putting the customer at the center of the transformation journey and leveraging data and insights, businesses can drive greater customer satisfaction, loyalty, and business success. Through the examples of Amazon and Starbucks, we can see the tangible benefits of taking a customer-centric approach to digital transformation. By learning from these companies and incorporating their strategies into their own efforts, organizations can position themselves for long-term success in the digital age.
SPECIAL BONUS: The very best change planners use a visual, collaborative approach to create their deliverables. A methodology and tools like those in Change Planning Toolkit™ can empower anyone to become great change planners themselves.
Image credit: misterinnovation.com
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